Senior Engineer - Machine Learning

Key Skills

Amazon Simple Storage Service (S3), AWS Sagemaker, Continuous Integration & Continuous Delivery - CI/CD, Docker, Generative AI, GitLab, Kubernetes-K8s, Lambda, Machine learning techniques, NoSQL, PyTorch, Scikit-learn, SQL

Job Description

Job Duties and Responsibilities

• Deploy end-to-end pipelines in classical ML, deep learning, forecasting, and GenAI applications such as chatbots.
• Architect scalable and efficient data processing workflows using AWS cloud services such as S3, Glue, EMR, Lambda, SageMaker, AWS Step Functions, and Amazon Forecast
• Establish and maintain CI/CD pipelines for automating model training, evaluation, and deployment processes, integrating with version control systems such as Git and CI/CD platforms like Jenkins or GitLab CI.
• Implement monitoring and logging solutions to track model performance, data drift, and other key metrics, leveraging tools such as Amazon CloudWatch, Elasticsearch, and AWS CloudTrail.
• Collaborate with DevOps teams to optimize infrastructure provisioning, configuration management, and deployment processes, ensuring scalability, reliability, and security of machine learning applications.

Skills, Experience and Requirements

Must have

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
• 4+ years of experience in machine learning engineering, data engineering, or related roles.
• Proficiency in Python and strong experience with machine learning libraries/frameworks such as PyTorch, Scikit-learn, and GenAI/Language models
• Prior experience in deployment of ML models at scale
• Solid understanding of data engineering concepts and experience with SQL and NoSQL databases
• Experience working with AWS services such as S3, Glue, EKS, Lambda, SageMaker, Step Functions, CloudFormation

Good to have

• Familiarity with DevOps practices and tools for infrastructure as code (e.g., Terraform), configuration management (e.g., Ansible), and CI/CD.
• Hands-on experience with containerization technologies (e.g., Docker) and container orchestration tools (e.g., Kubernetes).

Benefits

• Employee Stock Purchase
• Term Insurance
• Accident Insurance
• Health Insurance
• Training Reimbursement
• Gratuity
• Mobile and Internet Reimbursement
• Team Outings